EP1410331B1 - Verfahren und vorrichtung zur änderung eines numerischen bildes unter berücksichtigung des geräusches - Google Patents

Verfahren und vorrichtung zur änderung eines numerischen bildes unter berücksichtigung des geräusches Download PDF

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Publication number
EP1410331B1
EP1410331B1 EP02745485.9A EP02745485A EP1410331B1 EP 1410331 B1 EP1410331 B1 EP 1410331B1 EP 02745485 A EP02745485 A EP 02745485A EP 1410331 B1 EP1410331 B1 EP 1410331B1
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EP
European Patent Office
Prior art keywords
image
digital image
noise
corrected
zone
Prior art date
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EP02745485.9A
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English (en)
French (fr)
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EP1410331A2 (de
Inventor
Laurent Chanas
Frédéric Guichard
Lionel Moisan
Bruno Liege
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Dxo Labs SA
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Dxo Labs SA
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Priority claimed from FR0109291A external-priority patent/FR2827459B1/fr
Priority claimed from FR0109292A external-priority patent/FR2827460B1/fr
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0007—Image acquisition
    • G06T3/10—
    • G06T5/70—
    • G06T5/73—
    • G06T5/80—
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/00002—Diagnosis, testing or measuring; Detecting, analysing or monitoring not otherwise provided for
    • H04N1/00007—Diagnosis, testing or measuring; Detecting, analysing or monitoring not otherwise provided for relating to particular apparatus or devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/00002—Diagnosis, testing or measuring; Detecting, analysing or monitoring not otherwise provided for
    • H04N1/00026—Methods therefor
    • H04N1/00045—Methods therefor using a reference pattern designed for the purpose, e.g. a test chart
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/00002—Diagnosis, testing or measuring; Detecting, analysing or monitoring not otherwise provided for
    • H04N1/00071—Diagnosis, testing or measuring; Detecting, analysing or monitoring not otherwise provided for characterised by the action taken
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/387—Composing, repositioning or otherwise geometrically modifying originals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/40—Picture signal circuits
    • H04N1/40093—Modification of content of picture, e.g. retouching
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/46—Colour picture communication systems
    • H04N1/56—Processing of colour picture signals
    • H04N1/58—Edge or detail enhancement; Noise or error suppression, e.g. colour misregistration correction

Definitions

  • the present invention relates to a method and a system for modifying a digital image by taking into account its noise.
  • the invention relates to a method for calculating a transformed image from a digital image and formatted information relating to defects in a device chain.
  • the device chain includes image capturing devices and / or image rendering devices.
  • the appliance chain comprises at least one appliance.
  • the method includes the step of automatically determining characteristic data from the formatted information and / or the digital image.
  • the characteristic data are hereinafter referred to as the characteristic data of the noise.
  • the transformed image does not present a visible or annoying defect, including defects related to noise, for its subsequent use.
  • the formatted information comprises the characteristic data of the noise.
  • the method further comprises the step of implementing a transformation algorithm for producing an intermediate digital image.
  • the algorithm has the advantage of making desired modifications to the digital image but has the disadvantage of increasing the noise of the intermediate digital image.
  • the intermediate digital image is composed of the digital image.
  • the formatted information makes it possible to determine, for each image zone to be corrected, an image representation and a reference representation in a base relating to the image area to be corrected.
  • the set of parameter values of the parameterized operator forms the PR enhancement profile.
  • the formatted information may depend on variable feature values depending on the digital image, including digital image size.
  • the method further comprises the step of determining the value (s) of the variable characteristics, for the digital image.
  • the method is more particularly intended to calculate an image transformed to from a digital image and formatted information relating to defects of a device chain comprising at least one image rendering apparatus.
  • the rendering machine has a dynamic.
  • the transformed image has a dynamic.
  • the method further comprises the step of adapting the dynamics of the transformed image to the dynamics of said rendering apparatus.
  • the invention applies to the case of a digital image composed of color planes.
  • the application consists in applying the method according to the invention to each color plane.
  • An image transformed from the digital image is thus obtained.
  • the transformed image has the desired characteristics and a controlled noise level.
  • the invention relates to a system for calculating a transformed image from a digital image and formatted information relating to defects of a device chain.
  • the device chain includes image capturing devices and / or image rendering devices.
  • the appliance chain comprises at least one appliance.
  • the system includes computer processing means for automatically determining characteristic data from the formatted information and / or the digital image.
  • the data characteristics are hereinafter referred to as the characteristic data of the noise.
  • the transformed image has no visible or annoying defects, including noise-related defects, for its subsequent use.
  • the formatted information comprises the characteristic data of the noise.
  • the system further comprises computer processing means implementing a transformation algorithm for producing an intermediate digital image.
  • the algorithm has the advantage of making desired modifications to the digital image but has the disadvantage of increasing the noise of the intermediate digital image.
  • the intermediate digital image is composed of the digital image.
  • the formatted information depends on variable characteristic values according to the digital image, in particular the size of the digital image.
  • the system further comprises calculating means for determining the value (s) of the variable characteristics for the digital image.
  • the system is more particularly intended for calculating a transformed image from a digital image and formatted information relating to defects in a device chain comprising at least one device for restoring the image. 'picture.
  • the rendering machine has a dynamic.
  • the transformed image has a dynamic.
  • the system further comprises computer processing means for adapting the dynamics of the transformed image to the dynamics of the rendering apparatus.
  • a more complex P25 device such as a scanner / fax / printer, a photo printing Minilab, a video conferencing device can be considered a P25 device or more than one P25 device.
  • a P3 appliance chain is a set of P25 appliances.
  • the notion of P3 apparatus string may further include a notion of order.
  • the defect P5 of the apparatus P25 a defect related to the characteristics of the optics and / or the sensor and / or the electronics and / or the software integrated in a device P25; examples of defects P5 are for example distortion, blur, vignetting, chromatic aberration, color rendering, flash uniformity, sensor noise, grain, astigmatism, spherical aberration.
  • INUM digital image is an image captured or modified or restored by a P25 device.
  • the INUM digital image can come from a P25 device in the P3 appliance chain.
  • the INUM digital image can be for a P25 device in the device chain P3. More generally, the digital image INUM may come from and / or be intended for the P3 appliance chain.
  • animated images for example video, consisting of a sequence in the time of still images
  • digital image is called INUM: a fixed image of the image sequence.
  • IF-formatted information is called data related to defects P5 of one or more apparatuses P25 of the appliance chain P3 and making it possible to calculate a transformed image I-Transf taking into account defects P5 of the apparatus P25.
  • various methods based on measurements, and / or captures or restitution of references, and / or simulations can be used.
  • the appliance chain includes in particular at least one image capture apparatus and / or at least one image rendering apparatus.
  • the method includes the step of producing formatted information related to defects of at least one device in the chain.
  • the apparatus for capturing or restoring an image (I) comprises at least one fixed characteristic and / or a variable characteristic according to the image (I).
  • the fixed and / or variable characteristics are likely to be associated with one or more characteristic values, in particular the focal length and / or the focus and their associated characteristic values.
  • the method comprises the step of producing measured formatted information related to the defects of the apparatus from a measured field D (H).
  • the formatted information may include the measured formatted information.
  • IF formatted information is related to defects in a P3 device chain.
  • the device chain P3 comprises in particular at least one image capture apparatus and / or an image restoration apparatus.
  • the image processing means use the IF formatted information to modify the quality of at least one image from or intended for the P3 appliance chain.
  • the IF formatted information includes data characterizing P5 defects of the image capture apparatus, including distortion characteristics, and / or data characterizing defects in the image rendering apparatus, including distortion characteristics.
  • the method includes the step of providing at least one field of the standard format with the IF formatted information.
  • the field is designated by a field name.
  • the field contains at least one field value.
  • Image processing means for modifying the quality of digital images from or intended for a device chain.
  • the device chain comprises at least one image capture apparatus and / or at least one image rendering apparatus.
  • the image processing means implement implement formatted information related to the faults of at least one device in the appliance chain.
  • the formatted information depends on at least one variable. Formatted information to match some of the variables and identifiers.
  • the identifiers make it possible to determine the value of the variable corresponding to the identifier taking into account the identifier and the image. It follows from the combination of technical features that it is possible to determine the value of a variable, especially in the case where the physical meaning and / or the content of the variable are known only after the diffusion of the processing means. image. It also results from the combination of technical features that the time between two updates of the correction software can be spaced. It also results from the combination of technical features that the various economic actors that produce devices and / or image processing means can update their products independently of other economic actors, even if they radically change the characteristics of their products. product or can not force their client to update their products. It also results from the combination of technical features that a new functionality can be rolled out gradually starting with a limited number of economic actors and pioneering users.
  • variable characteristic CC a measurable and variable factor from one apparatus P25 to another but fixed from one INUM digital image to another captured, modified or restored by the same apparatus P25, for example, the focal length for a P25 camera with fixed focal length.
  • the IF formatted information may depend on at least one variable characteristic CC.
  • variable characteristic value VCC is called the value of the variable characteristic CC at the time of capture, modification or restitution of a given image.
  • An INUM digital image comprises a set of image elements called pixels Px-num.1 to Px-num.n regularly distributed on the surface of the RHUM image.
  • these pixels have the shape of squares but they could have a completely different form, circular or hexagonal for example; this depends on the design of the surfaces intended to carry the image in the image capture and rendering apparatus.
  • the pixels have been represented in a joined way but in reality, generally there is a spacing between the pixels.
  • the associated luminance at any point Px-num is vx-num.
  • the intermediate image I-Int comprises a set of pixels, similar to that of the INUM image but not necessarily, called intermediate pixels Px-int.1 to Px-int. each intermediate pixel is characterized by an intermediate position Px-int and an intermediate value vx-int.
  • the transformed image I-Trarisf also comprises a set of pixels called transformed pixels Px-tr.1 to Px-tr.n each transformed pixel is characterized by a transformed position Px-tr and a transformed value vx-tr.
  • the formatted information may relate to a limited number of transformed pixels and / or integrate values of variable characteristics according to the image (for example the focal length, the focus, the aperture, etc.), in this case. case there may be an additional step performed for example by interpolation so as to reduce to simple formatted information such as those of a device having no variable characteristics, so that the case of devices including variable focus is reduced to the case fixed focal length camera.
  • the formatted information may relate to a limited number of transformed pixels and / or values of variable characteristics depending on the image, in which case there may be an additional step performed for example by interpolation.
  • a function x ', y' f (x, y, t) where t is a variable characteristic (focal for example)
  • the formatted information can consist of a limited number of values (xi, yi, ti, f (xi, yi, ti)). It is then necessary to calculate an approximation for the other values of x, y, t other than the measurement points.
  • the formatted information could possibly consist of vectors making it possible to characterize the noise and / or the blur relating to a device and / or a chain of devices, and this for all combinations of the variable parameters of the device, in particular by using characteristic profiles of the defect in particular representation bases, in particular frequency representations such as, for example, Fourier transforms, wavelet transforms, etc.
  • frequency representations are compact and appropriate domains for the representation of physical phenomena related to noise and / or blur.
  • a frequency representation such as, for example, the Fourier transform
  • the formatted information may include data studied in a prior phase and relating to the devices used, but also any information in the format Exif format or otherwise that would provide information on the settings of the camera at the time of shooting (focal, focus , aperture, speed, flash ..).
  • the INUM digital image represents, for example, the capture of the monochromatic image of a white square on a black background.
  • the ideal profile (a step) is deformed.
  • the method of the invention makes it possible, using CAPP calculation means incorporating approximations according to, among other things, a desired final precision, to obtain on the transformed I-Transf image a square whose luminance value vx-tr in each point px-tr is corrected to the nearest approximations.
  • the application of the CAPP algorithm can in the case of noise and / or blur bring the original INUM image to a perfect or almost perfect image.
  • the same algorithm can also bring the INUM image to another possibly distorted image, but differently, so as to produce an image similar to a known type of noise and / or image blur (retro noise effect). .).
  • the same process also makes it possible to bring the INUM image back to a non-perfect image (in the sense of a white square on a black background as on the figure 2 ) but optimal in the eyes of the observer so that it is possible to compensate for defects in perception of the human eye.
  • characteristic data of the noise DcB For certain types of APP devices, notably image capture, it is possible to deduce characteristic data of the noise DcB from the formatted information. For example, this is particularly the case for the apparatus making it possible to provide variable influential characteristics on the noise such as the gain, the ISO, etc. The dependence between the noise and these characteristics will be indicated in the information formatted notably by means of functions. polynomial.
  • the INUM image is subdivided into a series of analysis zones (ZAN) that are not necessarily contiguous and can, if necessary, overlap.
  • ZAN analysis zones
  • the figure 3 represents an example of cutting.
  • a ZAN analysis zone may be of any shape and it is not necessary to analyze all the points inscribed in said ZAN analysis zone.
  • the method realizes a local luminance variation (VLL) measurement.
  • VLL local luminance variation
  • the set of local luminance variation measurements for all the ZAN analysis zones is then analyzed statistically to produce one or more data characteristic of the DcB noise and relative to the RHUM image.
  • VLL can be performed by calculating on a ZAN analysis zone, the maximum luminance deviation between the set of points.
  • VLL is 29, which represents the maximum difference between two pixels in the area.
  • Another way could be to calculate the standard deviation of the distribution relative to the luminance variation.
  • the set of measurements of local variation of luminance VLL can be analyzed statistically by creating a histogram of the frequencies of appearance of the variations.
  • This histogram an example of which is represented in figure 4b gate on the abscissa a quantization of the luminance deviations VLL according to the measurement accuracy on the noise.
  • the number of occurrences of a ZAN analysis zone giving the value VLL is totalized. In the example there were 22 ZAN analysis zones for which the local luminance variation measurement gave the value 50.
  • the profile of this histogram for a natural image for example a landscape image having a random distribution of patterns of different luminance, but homogeneous luminance over small areas of analysis, comprises a characteristic zone situated before the first local maximum ( Figure 4b, 4c ). If it is assumed that a natural image has a large number of small areas (size of a ZAN analysis area) for which the illumination is almost uniform, then the first local maximum of the histogram (d 'abscissa xm and ordinate fm) characterizes the average noise of the INUM image.
  • the characteristic data of the noise of the INUM image may consist of all the values of the histogram up to the first mode.
  • Another way to extract a more synthetic information of the noise characteristic consists, as shown on the figure 4c , assigning a mean noise value BM as the abscissa xb, between the origin and the first mode of the histogram (xm), for which the ordinate is a fraction of fm (typically its half).
  • the figure 5 represents a variant of calculation of the characteristic data of the noise DcB.
  • the invention provides for estimating simultaneously with the local luminance variation VLL information relating to the average luminance in said ZAN analysis zone (for example the average algebraic of the luminances on the area).
  • the method also provides, based on the quantification of the luminance images, to create classes that linearly or non-linearly subdivide the scale of the luminance. For 8-bit quantization the class maximum is 255; typically we will use between 5 and 10 classes (C1 .. Cn) of cutting of the luminance.
  • the choice of the division may be a function of the luminance histogram of the INUM image. Each class will have a cumulative VLL occurrence frequency histogram, so that the noise contained in the INUM image is analyzed by luminance slice.
  • the analysis of the average luminance and the local variation of luminance VLL in the zone ZAN-j makes it possible to determine the class Cj of membership of noise, and to extract from the data DcB the noise BM-j. In one way, a normalized Rj ratio between BM-j and VLL can be calculated. As shown on the figure 6 if Rj tends to 1 (where the local variation of luminance VLL is substantially of the same order as BM-j that is to say that noise is measured, then the luminance vx-tr of the transformed pixel Px-tr-j is taken mainly in INUM.
  • the luminance value of a transformed pixel can then be expressed as a function of the luminance of the pixel vx-num, the luminance of the pixel vx-int and the characteristic data of the noise.
  • the luminance value of a transformed pixel can be expressed as a function of the luminances of the pixel vx-num and its neighbors, the luminances of the pixel vx-int and of its neighbors and finally the characteristic data of the noise. .
  • This method has the advantage of taking in the intermediate image only the relevant information excluding the points for which the noise analyzed in the original INUM image is too important in the sense of a global statistical study of noise characterized by the DcB data.
  • the system according to the invention comprises in figure 3 , an SZ analysis zone selection device.
  • it comprises a computing device MC1 for calculating an intermediate pixel from a pixel Pi of the INUM image.
  • a calculation device dcb makes it possible to calculate the characteristic data of the noise DcB and to provide a coefficient Rj.
  • the computing device MC2 makes it possible to calculate the value of a transformed pixel, ie its luminance, from the values of the corresponding digital and intermediate pixels and the coefficient Rj.
  • the configurable model of the formatted information makes it possible to access characteristic profiles of the blur relating to an image representation RI and a reference representation RR. These profiles are expressed in a particular base including a frequency base B using for example a Fourier transform, a wavelet transform ...
  • the base B will be implicit or else filled in the formatted information.
  • a digital image for example INUM
  • the term base B and this non-exclusively, a base in the mathematical sense of the term of this vector space and / or a vector subspace thereof.
  • frequency is called an identifier relative to each element of the base.
  • Those skilled in the art include Fourier transformations and / or wavelet transformations as basic changes in image space.
  • the base B will preferably be chosen as a basis of representation of this subspace.
  • Another way of implementing the method in the sense of the invention is to choose a representation base of the optimal image in the sense of for example that of the calculation time.
  • This base may be chosen as a small size, each element of the base having a support of a few spatially located pixels in the INUM image (for example the Splines or the set of local variation operators Laplacian, Laplacian of Laplacian or derived from higher order ).
  • the measurement of the local variation of luminance VLL on the zone ZIC makes it possible thanks to the characteristic data of the noise DcB of INUM to calculate a coefficient Rj (device dcb2).
  • This coefficient will be coupled to the representations RI and RR (device pr) to generate a frequency profile PR enhancement relative to the zone ZIC.
  • This profile indicates the gain to be made to each frequency relative to the luminance information contained in the area to be corrected ZIC, to remove all or part of the blur.
  • the set of transformed image areas is then combined so as to obtain the deflated transformed image (I-Transf ID).
  • This combination makes it possible, for example, to provide solutions in the event of ZIC overlap, in particular to limit edge effects;
  • the image creation (I-Transf ID) as previously described, has the advantage of making the necessary modifications to the INUM image with respect to the blur, but has the disadvantage of increasing the noise in certain areas (including relatively uniform areas).
  • a second implementation of a method of the present invention is based on the exemplary embodiment of the system of the figure 7b . It allows a deflated image (I-Transf IDBC) with a controlled noise level.
  • the creation of the transformed image (I-Transf IDBC) implements a clipping procedure similar to that previously described in figure 6 , using the device dcb1 and the clipping device.
  • the intermediate image, as defined in the figure 6 is none other than the deflamed image (I-Transf ID).
  • the ratio between these two profiles can indicate the gain for each frequency to bring to RI to find RR.
  • the direct application of the calculated gain between RI and RR can generate undesirable behaviors, especially at high frequencies when the area to be corrected ZIC has a high level of noise. These phenomena are known to those skilled in the art by the effect of luminance oscillations called "ringing".
  • the method will estimate a profile RH between RR and RI and whose position is parameterized as a function of the noise in the analyzed zone ZIC.
  • the Figures 8a and 8b show two examples of profiles PR that can be generated according to the invention.
  • the difference between the profiles RI and RR shows the frequency loss introduced by the blur inherent in the device.
  • the figure 8a treats the case of a high noise level in the ZIC area; it will be advantageous to choose a profile RH between RI and RR and such that its effect is less towards the high frequencies (the end of RH will be confused with RI) which in this case present the information related to the noise in the picture.
  • the figure 8b deals with the case of a very low noise level in the ZIC zone; the high frequencies of profile RI therefore represent the signal and no longer noise.
  • We will then be interested in choosing an HR profile between RI and RR such that the gain between RH and RI remains significant even at high frequencies to reinforce the perception of details in the ZIC area.
  • RH can not exceed RR which is the ideal profile of the device but does not correspond to an image achievable by a real device.
  • RR the basis of representation chosen for the RR and RI representations is that of Fourier.
  • the abscissa axis carries the frequencies of the signal, that of ordinates carries the logarithm of the module of the Fourier transform.
  • One particular way of proceeding to compute an HR profile representation is to remain tangent at low frequency to the RR profile then ( Figures 8a, 8b ) to use a line to the extreme point characterizing the high frequencies.
  • Frequency rectification PR profile construction is carried out immediately by calculating the ratio of RH / RI for all frequencies.
  • the method of the invention is applicable to the processing of color images.
  • a color image is considered from the point of view of the software processing of the image as having as many images (or color planes) as there are basic colors in the image. This is how an IMrvb image is considered to have the three color planes Im-red, Im-green, Im-blue.
  • an IMcmjn image can be considered as comprising 4 color planes Im-cyan, Im-magenta, Im-yellow, Imnoir.
  • each color plane will be processed independently so as to obtain n transformed images that will recompose the different color planes of the transformed final image.
  • the method of the invention is applicable to the computation of a transformed digital image I-Transf, intended to be displayed via a known dynamic reproduction means ( figure 9a ) to create an I-REST image.
  • This means of reproduction for example a projector, intrinsically introduces blur at the moment of restitution, which is reflected in the figure 9b for example, by attenuating the profile of a staircase transition.
  • Figure 9c we have interest ( Figure 9c ) to modify upstream the dynamics of the transformed image so that the projected image has a profile closer to the ideal profile.
  • This dynamic modification is not always possible because of the quantification of the transformed image (generally 8 bits).
  • the process can reduce the overall dynamics of the transformed image (the image becomes less contrasted and therefore less energetic).
  • the method and system according to the invention can be used to reduce the cost of a device or a chain of devices: digital optics can be designed to produce IF formatted information relating to defects P5 of the device or to the appliance chain, use this formatted information to allow image processing means, integrated or not, to modify the quality of the images coming from or intended for the appliance or the appliance chain, so that the device combination or the device chain and image processing means can capture, modify or restore images of the desired quality with reduced cost.

Claims (22)

  1. Verfahren zum Erhalt eines umgewandelten Bilds (I-Transf) ausgehend von einem digitalen Bild (INUM) von einer Gerätekette (P3), wobei die Gerätekette (P3) Bilderfassungsgeräte (P25) und/oder Bildwiedergabegeräte aufweist, wobei die Gerätekette mindestens ein Gerät aufweist,
    wobei das Verfahren enthält:
    - den Schritt der automatischen Bestimmung charakteristischer Daten ausgehend von formatierten Informationen (IF) bezüglich von Fehlern (PS) der Gerätekette (P3) und/oder ausgehend vom digitalen Bild, wobei die charakteristischen Daten nachfolgend als charakteristische Rauschdaten (DcB) bezeichnet werden,
    - den Schritt der Berechnung des umgewandelten Bilds (I-Transf) ausgehend von den formatierten Informationen (IF) und von den charakteristischen Rauschdaten (DcB),
    wobei das Verfahren außerdem zur Bestimmung der charakteristischen Rauschdaten enthält:
    - den Schritt der Auswahl von Analysezonen (ZAN) im digitalen Bild (INUM), insbesondere abhängig von den Geräten (P25) der Gerätekette und/oder von den formatierten Informationen (IF),
    - den Schritt der Berechnung der lokalen Leuchtdichteschwankungen (VLL) in den Analysezonen (ZAN),
    - den Schritt der Ableitung der charakteristischen Rauschdaten (DcB) abhängig von einer statistischen Berechnung des Auftretens der lokalen Schwankungen über die Gesamtheit der Analysezonen (ZAN), wobei diese Ableitung folgendermaßen erfolgt:
    - es wird ein Histogramm (HC1, HC2, HC3) des Auftretens der lokalen Leuchtdichteschwankungen (VLL) erstellt, und
    - aus dem Histogramm wird zumindest ein Teil des Teils ausgewählt, der sich vor dem ersten lokalen Maximum einschließlich diesem befindet,
    wobei das Verfahren dadurch gekennzeichnet ist, dass es außerdem zur Auswahl der Analysezonen (ZAN) im digitalen Bild (INUM) den Schritt der Einordnung der Analysezonen entsprechend ihrer mittleren Leuchtdichte enthält, um Klassen (CI, C2, C3) zu erhalten, und dass es außerdem enthält:
    - den Schritt der Ableitung der charakteristischen Rauschdaten (DcB) für die zur gleichen Klasse gehörenden Analysezonen (ZANi, ZANj, ZANp),
    - den Schritt der Wiederholung des vorhergehenden Schritts für jede der Klassen (C1, C2, C3),
    damit so charakteristische Rauschdaten (DcB) abhängig von der Leuchtdichte erhalten werden.
  2. Verfahren nach Anspruch 1, wobei die formatierten Informationen (IF) die charakteristischen Rauschdaten (DcB) enthalten.
  3. Verfahren nach einem der Ansprüche 1 bis 2, wobei das Verfahren außerdem den Schritt der Anwendung eines Umwandlungsalgorithmus enthält, um ein digitales Zwischenbild (I-Int) herzustellen,
    wobei der Algorithmus den Vorteil hat, am digitalen Bild (INUM) gewünschte Änderungen vorzunehmen, aber den Nachteil hat, das Rauschen des digitalen Zwischenbilds (I-Int) zu erhöhen.
  4. Verfahren nach Anspruch 3, um ausgehend vom ausgehend vom digitalen Bild (INUM) erhaltenen digitalen Zwischenbild (I-Int) ein umgewandeltes Bild (I-Transf) zu berechnen, wobei das Verfahren außerdem den Schritt der Anwendung einer Funktion enthält, die zum Ziel hat, die Leuchtdichte des digitalen Bilds (INUM) zu ändern, und die zumindest als Argumente hat:
    - die Leuchtdichte (vx-int) eines Punkts des digitalen Zwischenbilds (px-int),
    - die Leuchtdichten (vx-num) einer Zone um den entsprechenden Punkt (px-num) des digitalen Bilds,
    - charakteristische Rauschdaten (DcB),
    damit so ein umgewandeltes Bild (I-Transf) erhalten wird, das die gewünschten Eigenschaften und einen kontrollierten Rauschpegel aufweist.
  5. Verfahren nach Anspruch 4, wobei das digitale Zwischenbild (I-Int) aus dem digitalen Bild (INUM) besteht.
  6. Verfahren nach einem der vorhergehenden Ansprüche, wobei das Verfahren insbesondere dazu bestimmt ist, ein umgewandeltes Bild (I-Transf ID) zu berechnen, in dem die ganze oder ein Teil der Unschärfe korrigiert wurde, wobei das Verfahren außerdem die folgenden Schritte enthält:
    - den Schritt der Auswahl von zu korrigierenden Bildzonen (ZIC) im digitalen Bild (INUM),
    - den Schritt der Erstellung, für jede so ausgewählte zu korrigierende Bildzone (ZIC), eines Kontrastverstärkungsprofils (PR) ausgehend von den formatierten Informationen (IF) und von den charakteristischen Rauschdaten (DcB),
    - den Schritt der Korrektur jeder so ausgewählten zu korrigierenden Bildzone (ZIC) abhängig vom Kontrastverstärkungsprofil (PR), um eine umgewandelte Bildzone zu erhalten,
    - den Schritt der Kombination der umgewandelten Bildzonen, um das umgewandelte Bild (I-Transf ID) des digitalen Bilds zu erhalten,
    damit so ein von Unschärfe befreites umgewandeltes Bild erhalten wird.
  7. Verfahren nach Anspruch 6, wobei die formatierten Informationen (IF) es ermöglichen, für jede zu korrigierende Bildzone (ZIC) eine Bilddarstellung (RI) und eine Bezugsdarstellung (RR) in einer Bank (B) bezüglich der zu korrigierenden Bildzone (ZIC) zu bestimmen, wobei das Verfahren so ist, dass es zur Erstellung eines Kontrastverstärkungsprofils (PR) ausgehend von den formatierten Informationen (IF) und vom Rauschen außerdem die folgenden Schritte enthält:
    - den Schritt der Bestimmung eines Profils (RH), ggf. unter Berücksichtigung des Rauschens, ausgehend von der Bilddarstellung (RI) und von der Bezugsdarstellung (RR),
    - den Schritt der Bestimmung eines parametrierten Operators, der es ermöglicht, von der Bilddarstellung (RI) zum Profil (RH) überzugehen,
    damit die Gesamtheit der Werte der Parameter des parametrierten Operators das Kontrastverstärkungsprofil (PR) bildet.
  8. Verfahren nach Anspruch 7, wobei das Verfahren außerdem zur Korrektur jeder zu korrigierenden Bildzone (ZIC) abhängig vom Kontrastverstärkungsprofil (PR) die folgenden Schritte enthält:
    - den Schritt der zumindest teilweisen Darstellung der zu korrigierenden Bildzone (ZIC) in der Bank (B),
    - den Schritt der Anwendung des parametrierten Operators an die am Ende des vorhergehenden Schritts erhaltene Darstellung, um eine korrigierte Darstellung der zu korrigierenden Bildzone (ZIC) zu erhalten,
    - den Schritt des Ersatzes der Darstellung der zu korrigierenden Bildzone (ZIC) durch die korrigierte Darstellung der zu korrigierenden Bildzone (ZIC), um eine umgewandelte Bildzone zu erhalten.
  9. Verfahren nach einem der Ansprüche 6 bis 8, wobei das Verfahren außerdem den Schritt der Berechnung eines Bilds mit einem kontrollierten Rauschpegel (I-Transf IDBC) ausgehend vom umgewandelten Bild enthält, indem eine Funktion angewendet wird, die zum Ziel hat, die Leuchtdichte des digitalen Bilds zu verändern und zumindest als Argumente hat:
    - die Leuchtdichte eines Punkts des umgewandelten digitalen Bilds,
    - die Leuchtdichten einer Zone um den entsprechenden Punkts des digitalen Bilds,
    - charakteristische Rauschdaten (DcB),
    damit so ein Bild mit entfernter Unschärfe (I-Transf IDBC) und mit einem kontrollierten Rauschpegel erhalten wird.
  10. Verfahren nach einem der vorhergehenden Ansprüche, wobei die formatierten Informationen von Werten von gemäß dem digitalen Bild variablen Eigenschaften abhängen, insbesondere von der Größe des digitalen Bilds, wobei das Verfahren außerdem den Schritt der Bestimmung des oder der Werte der variablen Eigenschaften für das digitale Bild enthält.
  11. Verfahren nach einem der vorhergehenden Ansprüche, wobei das Verfahren insbesondere dazu bestimmt ist, ein umgewandeltes Bild ausgehend von einem digitalen Bild und formatierten Informationen bezüglich von Fehlern einer Gerätekette zu berechnen, die mindestens ein Bildwiedergabegerät enthält, wobei das Wiedergabegerät eine Dynamik hat, wobei das umgewandelte Bild eine Dynamik hat, wobei das Verfahren außerdem den Schritt der Anpassung der Dynamik des umgewandelten Bilds an die Dynamik des Wiedergabegeräts enthält.
  12. System zum Erhalt eines umgewandelten Bilds (I-Transf) ausgehend von einem digitalen Bild (INUM) einer Gerätekette (P3), wobei die Gerätekette Bilderfassungsgeräte (P25) und/oder Bildwiedergabegeräte enthält, wobei die Gerätekette mindestens ein Gerät aufweist,
    wobei das System enthält
    - Datenverarbeitungseinrichtungen (dcb, MC1, MC2), um automatisch charakteristische Daten ausgehend von formatierten Informationen (IF) bezüglich von Fehlern (P5) der Gerätekette (P3) und/oder ausgehend vom digitalen Bild (INUM) zu bestimmen, wobei die charakteristischen Daten nachfolgend als charakteristische Rauschdaten (DcB) bezeichnet werden,
    - Datenverarbeitungseinrichtungen (dcb, MC1, MC2), um das umgewandelte Bild (I-Transf) ausgehend von den formatierten Informationen (IF) und von den charakteristischen Rauschdaten (DcB) zu berechnen,
    wobei die Datenverarbeitungseinrichtungen zur Bestimmung der charakteristischen Rauschdaten (DcB) außerdem enthalten
    - Auswahleinrichtungen (SZ), um im digitalen Bild (INUM) Analysezonen (ZAN) insbesondere abhängig von den Geräten der Gerätekette und/oder von den formatierten Informationen (IF) auszuwählen,
    - Recheneinrichtungen, um lokale Leuchtdichteschwankungen (VLL) in den Analysezonen (ZAN) zu berechnen,
    - Ableitungseinrichtungen, um die charakteristischen Rauschdaten (DcB) abhängig von einer statistischen Berechnung des Auftretens der lokalen Schwankungen in der Gesamtheit der Analysezonen (ZAN) abzuleiten,
    wobei die Ableitungseinrichtungen enthalten
    - Einrichtungen zur Erstellung eines Histogramms (HC1, HC2, HC3) des Auftretens der lokalen Leuchtdichteschwankungen (VLL),
    - Auswahleinrichtungen, um im Histogramm mindestens einen Teil des Teils auszuwählen, der sich vor dem ersten lokalen Maximum einschließlich diesem befindet,
    wobei das System dadurch gekennzeichnet ist, dass es außerdem zur Auswahl von Analysezonen (ZAN) im digitalen Bild (INUM) Einordnungseinrichtungen enthält, um die Analysezonen gemäß ihrer mittleren Leuchtdichte einzuordnen, um Klassen (C1, C2, C3) zu erhalten,
    wobei das System außerdem Datenverarbeitungseinrichtungen enthält, um:
    - die charakteristischen Rauschdaten (DcB) für die Analysezonen (ZANi, ZANj, ZANp) abzuleiten, die zur gleichen Klasse gehören,
    - den vorhergehenden Schritt für jede der Klassen (CI, C2, C3) zu wiederholen.
  13. System nach Anspruch 12, wobei die formatierten Informationen (IF) die charakteristischen Rauschdaten (DcB) aufweisen.
  14. System nach einem der Ansprüche 12 oder 13, wobei das System außerdem Datenverarbeitungseinrichtungen (MC1) enthält, die einen Umwandlungsalgorithmus anwenden, um ein digitales Zwischenbild (I-Int) herzustellen,
    wobei der Algorithmus den Vorteil hat, am digitalen Bild (INUM) gewünschte Veränderungen vorzunehmen, aber den Nachteil hat, das Rauschen des digitalen Zwischenbilds (I-Int) zu erhöhen.
  15. System nach Anspruch 14 zur Berechnung eines umgewandelten Bilds (I-Transf) ausgehend von dem ausgehend vom digitalen Bild (INUM) erhaltenen digitalen Zwischenbild (I-Int), wobei das System Recheneinrichtungen (MC2) enthält, die eine Funktion anwenden, die zum Ziel hat, die Leuchtdichte des digitalen Bilds zu verändern und mindestens als Argumente hat
    - die Leuchtdichte (vx-int) eines Punkts des digitalen Zwischenbilds (px-int),
    - die Leuchtdichten (vx-num) einer Zone um den entsprechenden Punkt (px-num) des digitalen Bilds,
    - charakteristische Rauschdaten (DcB).
  16. System nach Anspruch 15, wobei das digitale Zwischenbild (I-Int) aus dem digitalen Bild (INUM) besteht.
  17. System nach einem der Ansprüche 12 bis 16, wobei das System insbesondere dazu bestimmt ist, ein um die ganze oder einen Teil der Unschärfe korrigiertes umgewandeltes Bild (I-Transf ID) zu berechnen, wobei das System außerdem enthält:
    - Auswahleinrichtungen zur Auswahl von zu korrigierenden Bildzonen (ZIC) im digitalen Bild (INUM),
    - Recheneinrichtungen (dcb2, pr) zur Erstellung, für jede so ausgewählte zu korrigierende Bildzone (ZIC), eines Kontrastverstärkungsprofils (PR) ausgehend von den formatierten Informationen und von den charakteristischen Rauschdaten,
    - Datenverarbeitungseinrichtungen (zic), um:
    - jede so ausgewählte zu korrigierende Bildzone (ZIC) abhängig vom Kontrastverstärkungsprofil (PR) zu korrigieren, um eine umgewandelte Bildzone zu erhalten, und um
    - die umgewandelten Bildzonen so zu kombinieren, dass das umgewandelte Bild (I-Transf) des digitalen Bilds (INUM) erhalten wird.
  18. System nach Anspruch 17, wobei die formatierten Informationen (IF) es ermöglichen, für jede zu korrigierende Bildzone (ZIC) eine Bilddarstellung (RI) und eine Bezugsdarstellung (RR) in einer Bank (B) bezüglich der zu korrigierenden Bildzone (ZIC) zu bestimmen, wobei das System so ist, dass die Recheneinrichtungen zur Erstellung eines Kontrastverstärkungsprofils (PR) ausgehend von den formatierten Informationen (IF) und vom Rauschen außerdem Einrichtungen enthalten, um zu bestimmen:
    - ein Profil (RH), ggf. unter Berücksichtigung des Rauschens, ausgehend von der Bilddarstellung (RI) und von der Bezugsdarstellung (RR),
    - einen parametrierten Operator, der es ermöglicht, von der Bilddarstellung (RI) zum Profil (RH) überzugehen.
  19. System nach Anspruch 18, wobei die Datenverarbeitungseinrichtungen zur Korrektur jeder zu korrigierenden Bildzone (ZIC) abhängig vom Kontrastverstärkungsprofil (PR) Recheneinrichtungen enthalten, um:
    - zumindest zum Teil die zu korrigierende Bildzone (ZIC) in der Bank (B) darzustellen,
    - den parametrierten Operator an die Darstellung der zu korrigierenden Bildzone (ZIC) anzuwenden, um eine korrigierte Darstellung der zu korrigierenden Bildzone (ZIC) zu erhalten,
    - die Darstellung der zu korrigierenden Bildzone (ZIC) durch die korrigierte Darstellung der zu korrigierenden Bildzone (ZIC) zu ersetzen, um eine umgewandelte Bildzone zu erhalten.
  20. System nach einem der Ansprüche 17 bis 19, wobei das System außerdem Recheneinrichtungen enthält, um ausgehend vom umgewandelten Bild ein Bild mit einem kontrollierten Rauschpegel (I-Transf IDBC) zu berechnen, indem eine Funktion angewendet wird, die zum Ziel hat, die Leuchtdichte des digitalen Bilds zu verändern, und mindestens als Argumente hat:
    - die Leuchtdichte eines Punkts des umgewandelten digitalen Bilds,
    - die Leuchtdichten einer Zone um den entsprechenden Punkt des digitalen Bilds,
    - charakteristische Rauschdaten.
  21. System nach einem der Ansprüche 12 bis 20, wobei die formatierten Informationen von gemäß dem digitalen Bild variablen Eigenschaften abhängen, insbesondere von der Größe des digitalen Bilds, wobei das System außerdem Recheneinrichtungen enthält, um den oder die Werte der variablen Eigenschaften für das digitale Bild zu bestimmen.
  22. System nach einem der Ansprüche 12 bis 21, wobei das System insbesondere dazu bestimmt ist, ein umgewandeltes Bild ausgehend von einem digitalen Bild und von formatierten Informationen bezüglich von Fehlern einer Gerätekette zu berechnen, die mindestens ein Bildwiedergabegerät enthält, wobei das Wiedergabegerät eine Dynamik hat, wobei das umgewandelte Bild eine Dynamik hat, wobei das System außerdem Datenverarbeitungseinrichtungen enthält, um die Dynamik des umgewandelten Bilds an die Dynamik des Wiedergabegeräts anzupassen.
EP02745485.9A 2001-07-12 2002-06-05 Verfahren und vorrichtung zur änderung eines numerischen bildes unter berücksichtigung des geräusches Expired - Lifetime EP1410331B1 (de)

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FR0109291 2001-07-12
FR0109291A FR2827459B1 (fr) 2001-07-12 2001-07-12 Procede et systeme pour fournir a des logiciels de traitement d'image des informations formatees liees aux caracteristiques des appareils de capture d'image et/ou des moyens de restitution d'image
FR0109292A FR2827460B1 (fr) 2001-07-12 2001-07-12 Procede et systeme pour fournir, selon un format standard, a des logiciels de traitement d'images des informations liees aux caracteristiques des appareils de capture d'image et/ou des moyens de resti
FR0109292 2001-07-12
PCT/FR2002/001908 WO2003007243A2 (fr) 2001-07-12 2002-06-05 Procede et systeme pour modifier une image numerique en prenant en compte son bruit

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